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An Artificial Neural Network (ANN) model to predict the electric load profile for an HVAC system

机译:一种人工神经网络(ANN)模型,用于预测HVAC系统的电负载曲线

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A better management of the Heating, Ventilating and Air Conditioning (HVAC) systems and the integration of renewable energies are two ways to get a Net Zero Energy Buildings (NZEB). Thus, methods to predict the Electrical Load Demand (ELD) for the HVAC system are extremely important, to reach this goal. This paper describes the development and assessment of a fan-coil power demand predictive Artificial Neural Network (ANN) model for a characteristic laboratory inside a research centre located at Almeria (Southeast of Spain). As the model is aimed to be used as part of advanced building energy control schemes, some specific requirements, as a trade off between accuracy and simplicity, have been considered. The main consideration for improving new thermal comfort control system is how to save energy without affect the users' comfort. The performed experiments show a quick prediction with acceptable final results for a short-term prediction horizon using real data. Moreover, a detailed discussion of the obtained ANN model, which has been validated using real data saved from the research centre used as case-study, has been included.
机译:更好地管理加热,通风和空调(HVAC)系统以及可再生能源的整合是获得净零能量建筑(NEZEB)的两种方式。因此,预测HVAC系统的电负荷需求(ELD)的方法非常重要,以达到这种目标。本文介绍了在位于阿尔梅里亚(西班牙东南部)的研究中心内部特征实验室的风扇线圈功率需求预测人工神经网络(ANN)模型的开发和评估。由于该模型的旨在作为高级建筑能量控制方案的一部分,因此已经考虑了一些具体要求作为准确性和简单性之间的折扣。改善新型热舒适控制系统的主要考虑因素是如何节省能源而不会影响用户的舒适度。所执行的实验显示使用真实数据的短期预测地平线的可接受的最终结果快速预测。此外,已经包括使用从用作案例研究的研究中心保存的真实数据验证的所得ANN模型的详细讨论。

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